How to Choose the Best Evaluation Metric for Regression Problems

A comprehensive guide covering the most commonly used evaluation metrics used in regression and their utility in different scenarios

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Before building a regression model, it’s worth taking a moment to carefully think about how to evaluate it. A variety of factors will fall into that decision, including whether or not large errors should be punished more than small ones, or how comprehensible and intuitive the metric needs to be for stakeholders.

This article will cover the most commonly used evaluation metrics for regression problems. For each metric, we’ll go through an example use case as well, which will provide you with the information necessary to help you choose among them.

Regression

A regression problem is a supervised machine learning problem and characterized by the prediction of a continuous numerical output variable based on one or more input variables.

Imagine a regression model that aims to predict housing prices based on various features such as the number of bedrooms and bathrooms, square footage, location, and so on. Since we have various evaluation metrics at our disposal, it matters that we choose the one that aligns best…